Mobile phone data and the content generated by hundreds of millions of users on social media such as Twitter, or Facebook, present continuous data streams of human social activities, and offer an unprecedented opportunity to mine and understand the structure and dynamics of social and information behavior in various situations. In this workshop we will call attention to researching situations following large-scale emergencies, including natural disasters, terrorist attacks, and so on. These emergency events are now among the largest threats to national security. Over the last decade, natural disasters have affected more than 2.4 billions of people. There is an indisputably increasing need for new tools to strengthen disaster resilience at all levels of society.

How can we deal with data collected from heterogeneous and potentially biased sources? How can we properly understand social dynamics during emergencies? How can we turn such understanding into tools for decision makers? To better prepare for future emergencies, it is valuable to deeply understand the context within which the research can be applied.

The Web is perhaps the most complex system that we know. Its massive scale, complex dynamism, open richness, and social character mean that it may be more profitable to study it using tools and concepts appropriate for understanding nervous systems, organisms, ecosystems and society, rather than approaches more traditionally employed to engineer technology. Simultaneously, the scientists trying to understand this wide array of complex natural systems may have much to gain by considering the emergingstudy of the Web.

1st International Workshop on Scalable Computing For Real-Time Big Data Applications. This workshop aims at providing a venue for designers, practitioners, researchers, developers, and industrial/governmental partners to come together, present and discuss leading research results, use cases, innovative ideas, challenges, and opportunities that arise from real-time big data applications.

Workshop at a Glance: WHAT: Future of the Statistical Sciences Workshop WHEN: November 11-12, 2013 WHERE: Royal Statistical Society Offices, London, England About the Future of the Statistical Sciences Workshop The capstone event of the International...

In recent years, analysis of data from social media has provided a wealth of information about phenomena at societal scale, at least to the extent to which interactions, intentions and beliefs measured on-line reflect their real-world counterparts. Data from Twitter, Facebook, Google+, and Weblogs in general have been used to predict elections, opinions and attitudes, movie revenues, andoscillations in the stock market, to cite few examples. Similar data provided insights into the mechanisms driving the formation of groups of interests, topical communities, and the evolution of social networks. They also have been used to study polarization phenomena in politics, diffusion of information and the dynamics of collective attention.

The web has generated huge amounts of data at massivescale, but making sense of these datasets and representing them in acompact and easily-interpretable way remains very difficult. The goalof this challenge is to encourage innovative visualizations of webdata. To enable this visualization, the following several large-scale, easy-to-use, publicly-available datasets are prepared:

1. Web traffic data, including more than 200 million HTTP requestsfrom browsers to servers;2. Twitter data, including a sample of more than 22 million tweets;3. Social bookmarking data, consisting of about 430,000 bookmarked pages;4. Co-authorship of academic papers, consisting of about 21.5 million papersand 10.8 million authors

onur savas's insight:

Rules:1. For fairness, the visualization must be primarily based on the datathat we provide. Other datasets may be used to augment ours, but thesedatasets must be publicly-available and described in detail in thedocumentation (see #4 below).

2. The visualization must be a static image, and must be submitted asa PDF. In addition to the main PDF, please submit a PNG version at aresolution of about 640x480, for display on web pages, social mediasites, mobile devices, etc. This PNG version need not contain the fullvisualization, but should be an appropriate representation (e.g. asubset of the full PDF).

3. Please include a separate PDF file containing a description of thevisualization, including: (1) name(s), affiliation(s), and contactinformation of the creator(s), (2) the purpose of the visualization,(3) which dataset(s) were used, (4) a brief description of how thevisualizations was created, and (5) any other information you wouldlike to share with the judges.

4. By submitting your visualization, you agree to allow us to displayyour visualization at the conference and on the Web Science websiteand social media channels. (We will give proper attribution, ofcourse.) You also certify that you are the copyright holder of thevisualization and are authorized to give us this permission.

5. Entries are due by 11:59PM Hawaii time on April 15, 2014. Pleasee-mail your entry to David Crandall. (If you do not receive aconfirmation email within 24 hours, your entry has not been receivedand should be re-sent.)

DIMACS Workshop on Building Communities for Transforming Social Media Research Through New Approaches for Collecting, Analyzing, and Exploring Social Media DataApril 10 - 11, 2014 DIMACS Center, CoRE Building, Rutgers University

onur savas's insight:

Many interesting papers. For example: Matthew J. Salganik, "Wiki Surveys: Open and Quantifiable Social Data Collection."

Presented under the auspices of the DIMACS Special Focus on Information Sharing and Dynamic Data Analysis.

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